Different set.seed each run in R - r

Question

I want to "measure" which Regression Method is more robust to the outliers.
For this, I sum the variances of model coefficients. Each run, I generate data from t-distribution. I set.seed Ten times to have Ten specific data.
However, I also want to have Ten different seed each run. So, in total, I will have 10 sums of the variances. The code below is giving me one sum of the first (Ten different seed).
How can I do this?
#######################################
p <- 5
n <- 50
#######################################
FX <- function(seed, data) {
#for loops over a seed #
for (i in seed) {
set.seed(seed)
# generating data from t-distribution #
x<- matrix(rt(n*p,1), ncol = p)
y<-rt(n,1)
dat=cbind(x,y)
data<-as.data.frame(dat)
# performing a regression model on the data #
lm1 <- lm(y ~ ., data=data)
lm.coefs <- coef(lm1)
lad1 <- lad(y ~ ., data=data, method="BR")
lad.coefs <- coef(lad1)
}
# calculate variance of the coefficients #
return(`attr<-`(cbind(lmm=var(lm.coefs), lad=var(lad.coefs)), "seed", seed))
}
#######################################
seeds <- 1:10 ## 10 set seed to have diffrent data set from t-distribution #
res <- lapply(seeds, FX, data=data) # 10 diffrent variance of 10 data/model
sov <- t(sapply(res, colSums)) # put them in matrix
colSums(sov) # sum of 10 varainnces for each model.

Answer 1

Here is something closer to your intended results.
The code below fixes a key issues from your original code. It was not clear on what data was intended to be returned from the function.
This creates a vector of seeds numbers inside the function
This also creates a vector to inside the function to store the value of the variance of coefficients for each iteration of the loop. (not sure if is what you want).
I needed to comment out the lad function since I do not know which package this is from. (you would need to follow 2 from above to add this back in.
Some general clean of the code
p <- 5
n <- 50
FX <- function(seed, data) {
#for loops over a seed #
#Fixes the starting seed issue
startingSeed <- (seed-1)*10 +1
seeds <- seq( startingSeed, startingSeed+9)
#create vector to store results from loop iteration
lm.coefs <- vector(mode="numeric", length=10)
index <- 1
for (i in seeds) {
set.seed(i)
# generating data from t-distribution #
x<- matrix(rt(n*p,1), ncol = p)
y<-rt(n,1)
data<-data.frame(x, y)
# performing a regression model on the data #
lm1 <- lm(y ~ ., data=data)
lm.coefs[index] <- var(coef(lm1))
# lad1 <- lad(y ~ ., data=data, method="BR")
# lad.coefs <- coef(lad1)
index <- index +1
}
# calculate variance of the coefficients #
return(`attr<-`(cbind(lmm=lm.coefs), "seed", seed))
}
seeds <- 1:10 ## 10 set seed to have diffrent data set from t-distribution #
res <- lapply(seeds, FX, data=data) # 10 diffrent variance of 10 data/model
sov <- t(sapply(res, colSums)) # put them in matrix
colSums(sov) # sum of 10 varainnces for each model.
Hope this provides the answer or at least guidance to solve your problem.